| Library: | smoother |
| See also: | lpregest sker locpol |
| Quantlet: | lpregxest | |
| Description: | estimates a univariate regression function using local polynomial kernel regression with Quartic kernel. |
Fan and Marron (1994): Binning for local polynomials
Haerdle (1991): Smoothing Techniques
| Usage: | y = lpregxest (x,h {,p {,v}}) | |
| Input: | ||
| x | n x 2, the data. In the first column the independent, in the second column the dependent variable. | |
| h | scalar, bandwidth. If not given, the rule of thumb bandwidth computed by lpregrot is used. | |
| p | integer, order of polynomial. If not given, p=1 (local linear) is used. p=0 yields the Nadaraya-Watson estimator. p=2 (local quadratic) is the highest possible order. | |
| v | m x 1, values of the independent variable on which to compute the regression. If not given, x is used. | |
| Output: | ||
| mh | n x 2 or m x 2 matrix, the first column is the sorted first column of x or the sorted v, the second column contains the regression estimate on the values of the first column. | |
library("smoother")
library("plot")
;
x = 4.*pi.*(uniform(200)-0.5) ; independent variable
m = cos(x) ; true function
e = uniform(200)-0.5 ; error term
x = x~(m+e)
;
mh = lpregxest(x,1) ; estimate function
;
mh = setmask(mh, "line","blue")
m = setmask(sort(x[,1]~m) , "line","black","thin")
plot(x,mh,m)
The Nadaraya-Watson regession estimate (blue) using Quartic kernel and bandwidth h=1 and the true regression function (thin black) are pictured.
| Library: | smoother |
| See also: | lpregest sker locpol |